Loading packages
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(palmerpenguins)
glimpse(penguins)
## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex <fct> male, female, female, NA, female, male, female, male…
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…
Plot recreation
data frame
ggplot(data = penguins)
Map bill depth to the x-axis
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm))
Map bill length to the y-axis
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm,
y = bill_length_mm))
Represent each observation with a point
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm,
y = bill_length_mm)) +
geom_point()
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Map species to color of each point
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
colour = species)) +
geom_point()
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Title the plot “Bill depth and length”
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
colour = species)) +
geom_point() +
labs(title = "Bill depth and length")
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo
Penguins”
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
colour = species)) +
geom_point() +
labs(title = "Bill depth and length",
subtitle = "Dimensions for Adelie,
Chinstrap, and Gentoo Penguins")
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Label the legend “Species”
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
colour = species)) +
geom_point() +
labs(title = "Bill depth and length",
subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
x = "Bill depth (mm)", y = "Bill length",
colour = "Species")
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Add a caption for the data source
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
colour = species)) +
geom_point() +
labs(title = "Bill depth and length",
subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
x = "Bill depth (mm)", y = "Bill length", colour = "Species",
caption = "Source: Palmer Station LTER")
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Shape
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm, colour = species,
shape = island)) +
geom_point() + scale_colour_viridis_d()
## Warning: Removed 2 rows containing missing values (`geom_point()`).
size
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm, colour = species, shape = species,
size = body_mass_g)) +
geom_point() + scale_colour_viridis_d()
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Alpha (transparency)
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm, colour = species,
shape = species, size = body_mass_g, alpha = flipper_length_mm)) +
geom_point() + scale_colour_viridis_d()
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Mapping and Setting
#Mapping: Determine the size, alpha, etc. of points based on the values of a variable in the data goes into aes()
#Setting: Determine the size, alpha, etc. of points not based on the values of a variable in the data goes into geom_*()
Faceting
ggplot(penguins) +
aes(x = bill_depth_mm,
y = bill_length_mm) +
geom_point() +
facet_grid(species ~ island)
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Facet 2
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + geom_point() +
facet_grid(species ~ sex)
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Facet 3
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + geom_point() +
facet_grid(sex ~ species)
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Facet 4
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + geom_point() +
facet_wrap(~ species)
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Facet 5
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + geom_point() +
facet_wrap(~ species, ncol = 2)
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Facet 6
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + geom_point() +
facet_grid(. ~ species)
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Facet & color
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm, color = species)) +
geom_point() + facet_grid(species ~ sex) + scale_color_viridis_d()
## Warning: Removed 2 rows containing missing values (`geom_point()`).
Face and color, no legend
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm, color = species)) +
geom_point() + facet_grid(species ~ sex) + scale_color_viridis_d() +
guides(color = "none")
## Warning: Removed 2 rows containing missing values (`geom_point()`).
visualising numeric variables
loading data
library(openintro)
## Loading required package: airports
## Loading required package: cherryblossom
## Loading required package: usdata
glimpse(loans_full_schema)
## Rows: 10,000
## Columns: 55
## $ emp_title <chr> "global config engineer ", "warehouse…
## $ emp_length <dbl> 3, 10, 3, 1, 10, NA, 10, 10, 10, 3, 1…
## $ state <fct> NJ, HI, WI, PA, CA, KY, MI, AZ, NV, I…
## $ homeownership <fct> MORTGAGE, RENT, RENT, RENT, RENT, OWN…
## $ annual_income <dbl> 90000, 40000, 40000, 30000, 35000, 34…
## $ verified_income <fct> Verified, Not Verified, Source Verifi…
## $ debt_to_income <dbl> 18.01, 5.04, 21.15, 10.16, 57.96, 6.4…
## $ annual_income_joint <dbl> NA, NA, NA, NA, 57000, NA, 155000, NA…
## $ verification_income_joint <fct> , , , , Verified, , Not Verified, , ,…
## $ debt_to_income_joint <dbl> NA, NA, NA, NA, 37.66, NA, 13.12, NA,…
## $ delinq_2y <int> 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0…
## $ months_since_last_delinq <int> 38, NA, 28, NA, NA, 3, NA, 19, 18, NA…
## $ earliest_credit_line <dbl> 2001, 1996, 2006, 2007, 2008, 1990, 2…
## $ inquiries_last_12m <int> 6, 1, 4, 0, 7, 6, 1, 1, 3, 0, 4, 4, 8…
## $ total_credit_lines <int> 28, 30, 31, 4, 22, 32, 12, 30, 35, 9,…
## $ open_credit_lines <int> 10, 14, 10, 4, 16, 12, 10, 15, 21, 6,…
## $ total_credit_limit <int> 70795, 28800, 24193, 25400, 69839, 42…
## $ total_credit_utilized <int> 38767, 4321, 16000, 4997, 52722, 3898…
## $ num_collections_last_12m <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ num_historical_failed_to_pay <int> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ months_since_90d_late <int> 38, NA, 28, NA, NA, 60, NA, 71, 18, N…
## $ current_accounts_delinq <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ total_collection_amount_ever <int> 1250, 0, 432, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ current_installment_accounts <int> 2, 0, 1, 1, 1, 0, 2, 2, 6, 1, 2, 1, 2…
## $ accounts_opened_24m <int> 5, 11, 13, 1, 6, 2, 1, 4, 10, 5, 6, 7…
## $ months_since_last_credit_inquiry <int> 5, 8, 7, 15, 4, 5, 9, 7, 4, 17, 3, 4,…
## $ num_satisfactory_accounts <int> 10, 14, 10, 4, 16, 12, 10, 15, 21, 6,…
## $ num_accounts_120d_past_due <int> 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, …
## $ num_accounts_30d_past_due <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ num_active_debit_accounts <int> 2, 3, 3, 2, 10, 1, 3, 5, 11, 3, 2, 2,…
## $ total_debit_limit <int> 11100, 16500, 4300, 19400, 32700, 272…
## $ num_total_cc_accounts <int> 14, 24, 14, 3, 20, 27, 8, 16, 19, 7, …
## $ num_open_cc_accounts <int> 8, 14, 8, 3, 15, 12, 7, 12, 14, 5, 8,…
## $ num_cc_carrying_balance <int> 6, 4, 6, 2, 13, 5, 6, 10, 14, 3, 5, 3…
## $ num_mort_accounts <int> 1, 0, 0, 0, 0, 3, 2, 7, 2, 0, 2, 3, 3…
## $ account_never_delinq_percent <dbl> 92.9, 100.0, 93.5, 100.0, 100.0, 78.1…
## $ tax_liens <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ public_record_bankrupt <int> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ loan_purpose <fct> moving, debt_consolidation, other, de…
## $ application_type <fct> individual, individual, individual, i…
## $ loan_amount <int> 28000, 5000, 2000, 21600, 23000, 5000…
## $ term <dbl> 60, 36, 36, 36, 36, 36, 60, 60, 36, 3…
## $ interest_rate <dbl> 14.07, 12.61, 17.09, 6.72, 14.07, 6.7…
## $ installment <dbl> 652.53, 167.54, 71.40, 664.19, 786.87…
## $ grade <fct> C, C, D, A, C, A, C, B, C, A, C, B, C…
## $ sub_grade <fct> C3, C1, D1, A3, C3, A3, C2, B5, C2, A…
## $ issue_month <fct> Mar-2018, Feb-2018, Feb-2018, Jan-201…
## $ loan_status <fct> Current, Current, Current, Current, C…
## $ initial_listing_status <fct> whole, whole, fractional, whole, whol…
## $ disbursement_method <fct> Cash, Cash, Cash, Cash, Cash, Cash, C…
## $ balance <dbl> 27015.86, 4651.37, 1824.63, 18853.26,…
## $ paid_total <dbl> 1999.330, 499.120, 281.800, 3312.890,…
## $ paid_principal <dbl> 984.14, 348.63, 175.37, 2746.74, 1569…
## $ paid_interest <dbl> 1015.19, 150.49, 106.43, 566.15, 754.…
## $ paid_late_fees <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
selecting data
loans <- loans_full_schema %>%
select(loan_amount, interest_rate, term, grade,
state, annual_income, homeownership, debt_to_income)
glimpse(loans)
## Rows: 10,000
## Columns: 8
## $ loan_amount <int> 28000, 5000, 2000, 21600, 23000, 5000, 24000, 20000, 20…
## $ interest_rate <dbl> 14.07, 12.61, 17.09, 6.72, 14.07, 6.72, 13.59, 11.99, 1…
## $ term <dbl> 60, 36, 36, 36, 36, 36, 60, 60, 36, 36, 60, 60, 36, 60,…
## $ grade <fct> C, C, D, A, C, A, C, B, C, A, C, B, C, B, D, D, D, F, E…
## $ state <fct> NJ, HI, WI, PA, CA, KY, MI, AZ, NV, IL, IL, FL, SC, CO,…
## $ annual_income <dbl> 90000, 40000, 40000, 30000, 35000, 34000, 35000, 110000…
## $ homeownership <fct> MORTGAGE, RENT, RENT, RENT, RENT, OWN, MORTGAGE, MORTGA…
## $ debt_to_income <dbl> 18.01, 5.04, 21.15, 10.16, 57.96, 6.46, 23.66, 16.19, 3…
Histogram
ggplot(loans) + aes(x = loan_amount) +
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
customising and labeling histogram
ggplot(loans, aes(x = loan_amount)) + geom_histogram(binwidth = 5000) +
labs(x = "Loan amount ($)", y = "Frequency", title = "Amounts of Lending Club loans" )
Fill with a categorical variable
ggplot(loans, aes(x = loan_amount, fill = homeownership)) +
geom_histogram(binwidth = 5000, alpha = 0.5) +
labs(x = "Loan amount ($)",y = "Frequency",title = "Amounts of Lending Club loans")
Facet with a categorical variable
ggplot(loans, aes(x = loan_amount, fill = homeownership)) + geom_histogram(binwidth = 5000) +
labs(x = "Loan amount ($)",y = "Frequency",title = "Amounts of Lending Club loans") +
facet_wrap(~ homeownership, nrow = 3)
density
ggplot(loans, aes(x = loan_amount)) +
geom_density()
adjusting bandwidth
ggplot(loans, aes(x = loan_amount)) +
geom_density(adjust = 0.5)
customising density plot
ggplot(loans, aes(x = loan_amount)) +
geom_density(adjust = 2) +
labs( x = "Loan amount ($)", y = "Density", title = "Amounts of Lending Club loans" )
adding a categorical variable
ggplot(loans, aes(x = loan_amount, fill = homeownership)) +
geom_density(adjust = 2, alpha = 0.5) +
labs(x = "Loan amount ($)",y = "Density",title = "Amounts of Lending Club loans", fill = "Homeow")
box plot
ggplot(loans, aes(x = interest_rate)) +
geom_boxplot()
Customizing box plots
ggplot(loans, aes(x = interest_rate)) +geom_boxplot() +labs(x = "Interest rate (%)",y = NULL,
title = "Interest rates of Lending Club loans") +
theme( axis.ticks.y = element_blank(), axis.text.y = element_blank() )
adding a categorical variable
ggplot(loans, aes(x = interest_rate,
y = grade)) +
geom_boxplot() +
labs(x = "Interest rate (%)",y = "Grade",title = "Interest rates of Lending Club loans",subtitle = "by grade of loan")
Scatter plot
ggplot(loans, aes(x = debt_to_income, y = interest_rate)) +
geom_point()
## Warning: Removed 24 rows containing missing values (`geom_point()`).
Hex plot
ggplot(loans %>% filter(debt_to_income < 100),
aes(x = debt_to_income, y = interest_rate)) +
geom_hex()
Visualising categoric variable
ggplot(loans, aes(x = homeownership)) +
geom_bar()
Segmented bar plot
ggplot(loans, aes(x = homeownership, fill = grade)) +
geom_bar(position = "fill")
Customizing bar plots
ggplot(loans, aes(y = homeownership, fill = grade)) + geom_bar(position = "fill") +
labs( x = "Proportion", y = "Homeownership", fill = "Grade", title = "Grades of Lending Club loans", subtitle = "and homeownership of lendee")
violin plot
ggplot(loans, aes(x = homeownership, y = loan_amount)) +
geom_violin()
Ridge plot
library(ggridges)
ggplot(loans, aes(x = loan_amount, y = grade, fill = grade, color = grade)) +
geom_density_ridges(alpha = 0.5)
## Picking joint bandwidth of 2360
